Discovery & classification
The agent reads schema metadata and profiles columns locally, then reports which columns look sensitive — names and classes only, never sample values.
Everything between “we need test data” and a certified dataset in QA
Discover, subset, mask, certify and provision — every row-level step in your network.
A hosted control plane for people and policy; a customer-resident engine for data. Here is what each part does.
Capabilities
Subsetting
Entity-driven subsets with referential closure, date windows and filters, so a few thousand members bring their claims, or a few hundred customers bring their accounts.
Masking & pseudonymization
Versioned masking policies with deterministic, keyed transformations so the same input masks the same way in every system. Keys stay in your secret store.
Synthetic generation
Generate records from pack templates and scenarios when you need edge cases production does not have, or when no source access is appropriate.
Certification
Automated gates for coverage, masking, referential integrity, schema, quality and provenance. Masked is not the same as certified — failed datasets are never provisioned.
Provisioning & refresh
Deliver to DEV, QA, SIT, UAT and performance targets, schedule refreshes, and expire datasets according to retention policy.
Governance & audit
Role-based access, approvals with separation of duties, and an append-only, hash-chained audit trail. Evidence stays in your environment; we keep references.
APIs & CLI
Everything in the console is available through a versioned REST API and the datanivra CLI, so datasets can be requested from CI/CD pipelines.
From request to certified dataset
Text description
- Prepare (Your environment): Discover → Classify → Subset → Mask
Connection: Same job, same environment
- Prove & deliver (Your environment): Synthesize → Validate → Certify → Provision
Prefer a conversation? Request a demo or contact sales — optional, never required.